Developing improved techniques for GPR guided wave data analysis

This study was conducted to investigate how limiting the allowable velocity range when analyzing guided wave data will affect the results of dielectric permittivity and thickness estimation. We conducted four sets of Experiments: an underlying layer of saturated organic loam is covered by incremental layers of the dry organic loam; a base layer of dry organic loam covered by the saturated organic loam; a saturated basal layer of silt with overlying dry silt layers, and a base layer of dry silt overlain by saturated silt layers to conduct a comparative study and perform three surveys for each of these three frequencies at 250 MHz, 500 MHz, and 1000 MHz It is concluded that from the aspect of water content, the location of the selected points has no effect on the final result; and from the aspect of soil structure, in most cases, for organic loam layers, the lower the starting phase velocity, the more accurate the results; for silt layers, the pattern is the opposite, the higher the starting point, the more accurate the results. For the fundamental mode, choosing the maximum starting phase velocity is usually best or equivalent to choosing a lower starting phase velocity. For some wet soils that have low attenuation, it may be better to choose a lower starting phase velocity. The error of inversion is less for lower starting phase velocities, so this should be considered when evaluating the accuracy of inversion estimates --Abstract, page iii

Prediction of Soil Water Content and Electrical Conductivity using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and tempo-rally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agricul-ture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8,000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and al-falfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was per-formed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil

Prediction of Soil Water Content and Electrical Conductivity using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and tempo-rally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agricul-ture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8,000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and al-falfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was per-formed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil

Changes in Chemical Composition of Flaxseed Oil during Thermal-Induced Oxidation and Resultant Effect on DSC Thermal Properties

To investigate the changes in chemical composition of flaxseed oil during thermal-induced oxidation and the resultant effect on thermal properties, samples with different oxidation levels were obtained by being heated at 180 °C for two hours and four hours. The oxidation degree was evaluated using peroxide value (PV), extinction coefficient at 232 nm and 268 nm (K232 and K268), and total polar compounds (TPC). Using chromatography, the fatty acid profile and triacylglycerol (TAG) profile were examined. Differential scanning calorimetry (DSC) was used to determine the crystallization and melting profiles. Thermal-induced oxidation of flaxseed oil led to a significant increase (p 232, K268, and TPC, but the relative content of linolenic acid (Ln) and LnLnLn reduced dramatically (p Ton) of the crystallization curve was highly correlated with K232, TPC, and the relative content of LnLnLn (p Toff) of the melting curve was highly correlated with the relative content of most fatty acids (p < 0.05). This finding provides a new way of rapid evaluation of oxidation level and changes of chemical composition for flaxseed oils using DSC

Nonconvex L₁/₂- regularized nonlocal self-similarity denoiser for compressive sensing based CT reconstruction

Compressive sensing (CS) based computed tomography (CT) image reconstruction aims at reducing the radiation risk through sparse-view projection data. However, it is challenging to achieve satisfying image quality from incomplete projections. Recent works demonstrate the promising potential of nonconvex L1/2-norm in CS problem, while the applications on medical imaging are constrained by its nonconvexity. In this paper, we develop an L1/2-regularized nonlocal self-similarity (NSS) denoiser based CT reconstruction model, which combines with low-rank approximation and group sparse coding (GSC) framework. Concretely, we firstly split the CT reconstruction problem into two subproblems, then improve CT image quality furtherly using our proposed denoiser. Instead of optimizing the nonconvex problem under the perspective of GSC, we particularly reconstruct CT image via low-rank minimization based on two simple yet essential schemes, which build the equivalent relationship between GSC based denoiser and low-rank minimization. Furtherly, the weighted singular value thresholding (WSVT) operator is utilized to optimize the resulting nonconvex L1/2 minimization problem. Following this, our proposed denoiser is integrated with the CT reconstruction problem by alternating direction method of multipliers (ADMM) framework. Extensive experimental results on typical clinical CT images have demonstrated that our approach can further achieve better performance than popular approaches.This work was supported in part by the Educational Commission of Hunan Province of China under grant 21B0466, the National Natural Science Fund for Youth Programs under Grant 61906067, and the China Postdoctoral Science Foundation under Grant 2019M651415 and Grant 2020T130191